A hypothetical data analysis project, answering a business question for a fictional bikeshare company, analyzing customer behavior and concluding with recommendations to assist in converting casual customers into annual members.

Executive Summary

This report analyzes historical data on Cyclistic users to provide data-driven insights for a digital marketing campaign aimed at converting casual riders into annual members. The top three recommendations focus on leveraging existing rider behaviors to encourage membership sign-ups.

Key Findings

  • Casual riders primarily use Cyclistic for leisure: They take longer trips (averaging an hour) and ride mostly on weekends, particularly Saturday afternoons.

  • Annual members are predominantly commuters: Their rides are shorter (averaging 15 minutes) and occur mainly during weekday commuting hours (8 AM and 5 PM).

  • Demographic differences: Members tend to be slightly older, peaking in their early 30s, while casual riders peak in their early 20s.

  • Location trends: Members’ trips are concentrated in downtown Chicago’s central business district (The Loop), while casual riders frequent popular leisure destinations.

Key Visuals

Compare the map of each group’s top 10 stations, especially the top casual destinations, with the Chicago sightseeing map.

Recommendation Summary

Leisure-Focused Marketing: Position membership as a way to enhance casual riders’ existing leisure habits by emphasizing benefits such as unlimited rides and extended ride times.

Targeted Digital Advertising: Deploy ads on platforms frequented by casual riders, particularly those related to entertainment, travel, and dining, and use geo-targeted ads near high-traffic leisure stations.

Promotional Incentives: Offer weekend-focused memberships, seasonal discounts, referral bonuses, and strategic partnerships with major Chicago events and leisure attractions.



Methodology

This analysis follows the framework outlined in Google’s Data Analytics certificate, structured as follows:

  1. Ask: A clear statement of the business task – converting casual riders into annual members.
  2. Prepare: Used cleaned historical data from 2019 detailing trip durations, ride frequencies, and popular station destinations.
  3. Process: Perform and document data cleaning, manipulation, and exploratory analysis.
  4. Analyze: A summary of the data analysis, identifying behavioral trends and differences between customer segments.
  5. Share: Supporting visualizations and key findings.
  6. Act: Provide top three actionable recommendations based on findings.

Scenario

See ./resources/Case Study 1_How does a bike-share navigate speedy success.pdf. Quoted from the Google Data Analytics Capstone:

You are a junior data analyst working on the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes dierently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Characters and teams

● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use the bikes to commute to work each day.

● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.

● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals—as well as how you, as a junior data analyst, can help Cyclistic achieve them.

● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

About the company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.

Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members.

Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a solid opportunity to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs.

Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

Note that data-privacy issues prohibit you from using riders’ personally identifiable information. This means that you won’t be able to connect pass purchases to credit card numbers to determine if casual riders live in the Cyclistic service area or if they have purchased multiple single passes.

Three questions will guide the future marketing program:

  1. How do annual members and casual riders use Cyclistic bikes differently?
  2. Why would casual riders buy Cyclistic annual memberships?
  3. How can Cyclistic use digital media to influence casual riders to become members?

Moreno has assigned you the first question to answer: How do annual members and casual riders use Cyclistic bikes differently?

Ask

Deliverable: A clear statement of the business task

How do annual members and casual riders use Cyclistic bikes differently? Base the analysis on Cyclistic historical bike trip data.

Prepare

Deliverable: A description of all data sources used

The analysis incorporates cleaned ride data from all four quarters of 2019, detailing trip durations, ride frequencies, station destinations, and date-time.

I used data for 2019 because it has info on gender and birthyear. Pretend that was last year and the data is current and useful.

Data Dictionary

ride_id
     Unique identifier for each ride.

started_at
     Timestamp indicating when the ride started (YYYY-MM-DD HH:MM:SS).

ended_at
     Timestamp indicating when the ride ended (YYYY-MM-DD HH:MM:SS).

rideable_type
     Unique identifier for the bicycle.

start_station_id
     Unique identifier for the starting station.

start_station_name
     Name of the starting station.

end_station_id
     Unique identifier for the ending station.

end_station_name
     Name of the ending station.

member_casual
     Type of rider (member for annual subscribers, casual for non-members).

gender
     Gender of the rider (if available).

birthyear
     Birth year of the rider (if available).

date
     Date of the ride (YYYY-MM-DD).

month
     Month when the ride occurred (1–12).

day
     Day of the month when the ride occurred (1–31).

year
     Year when the ride occurred (YYYY).

day_of_week
     Day of the week when the ride occurred (Monday–Sunday).

season
     Season in which the ride took place (Winter, Spring, Summer, Fall).

hour
     Hour when the ride started (0–23).

ride_length
     Duration of the ride in minutes.

start_lat
     Latitude coordinate of the starting location.

start_lng
     Longitude coordinate of the starting location.

end_lat
     Latitude coordinate of the ending location.

end_lng
     Longitude coordinate of the ending location.

Google linked to data provided by Lyft and Divvy from their Chicago operations.

The data has been made available by Motivate International Inc. under this license.

Process

Deliverable: Documentation of any cleaning or manipulation of data

Google provided a starter template to clean the data. See data_cleaning_v1.R.

Google’s template covered only Q1 2019 and Q1 2020. I wanted to have all of 2019 available, so I modified the script to clean individual quarters of Cyclistic data. To join Q1 2019 and Q1 2020, Google’s template removed trip duration, gender, and birth year from 2019, station latitude and longitude from 2020. I wanted to keep that info, so I dropped 2020 (lacking gender and birthyear), and added 2020’s lat/lng to 2019.

Data cleaning consisted of:

● Extracting station latitude and longitude from 2020 and adding it to start and end stations in 2019 (labeling all but 366 of almost 4 million rows).

● Removing “tripduration” from 2019.

● Consolidating the “member_casual” column from four labels to two (“member” and “casual”), instead of the original two names for members (“member” and “Subscriber”) and two names for casual riders (“Customer” and “casual”).

● Extracting day, month, year, season, and day of the week from the start date and creating columns for each, to provide additional opportunities to aggregate the data.

● Adding a calculated field (“ride_length”) for length of ride since the 2020Q1 data did not have the “tripduration” column. This briefly catastrophically halted data analysis, as ride length was calculated in minutes for 2019, and seconds in 2020.

● Remvoing rides where ride_length shows up as negative, including several hundred rides where the company took bikes out of circulation for Quality Control reasons.

Analyze

Deliverable: A summary of the data analysis

Casual riders’ trips last approximately four times longer than those of members (one hour vs 15 mins).

Members take significantly more trips overall, with routine weekday usage.

Members peak during commuting hours; casual riders peak on weekend afternoons.

Members favor The Loop for work-related trips, while casual riders frequent leisure destinations such as Shedd Aquarium and Theater on the Lake.

See ./resources/scratch_pad.Rmd for further insights.

Share

Deliverable: Supporting visualizations and key findings

Refer to the Executive Summary.

There are three times more member rides than casual rides (3 million vs 1 million).

Ridership peaks in August and nadirs in February, porportionally for both groups. Winter is dead. Crickets for activity.

Casual riders ride late morning, peaking late afternoon, especially Saturday, quite a bit Sunday. Weekend Warriors. Members have two peaks - weekdays at 8am and 5pm. Commuters. They also ride roughly the same on weekends, a gentle curve from late morning to late afternoon.

Top 10 stations

Members riders go to the “Loop”. They use Cyclistic for commuting to places of employment.

Casual riders seem to hit tourist / leisure destinations (I assume? Must research Chicago landmarks. “Milleniumn Park”, “Shedd Aquarium”, “Theater on the Lake”, “Dusable Harbor”, and “Adler Planetarium” sound like destinations for the occasional bike rider).

Top 10 Member Peak Stations
start_station_name Member_Rides
Canal St & Adams St 50575
Clinton St & Madison St 45990
Clinton St & Washington Blvd 45378
Columbus Dr & Randolph St 31370
Franklin St & Monroe St 30832
Kingsbury St & Kinzie St 30654
Daley Center Plaza 30423
Canal St & Madison St 27138
Michigan Ave & Washington St 25468
LaSalle St & Jackson Blvd 23021
Top 10 Casual Peak Stations
start_station_name Casual_Rides
Streeter Dr & Grand Ave 53104
Lake Shore Dr & Monroe St 39238
Millennium Park 21749
Michigan Ave & Oak St 21388
Shedd Aquarium 20617
Lake Shore Dr & North Blvd 18952
Theater on the Lake 15024
Dusable Harbor 12546
Michigan Ave & Washington St 12228
Adler Planetarium 11928

Compare the above map, especially the top casual destinations, with the Chicago sightseeing map.

Demographics

Members are slightly older, peaking in 30-35. Casual in 25-30.

More men ride than women. Maybe an opportunity there.

## Act

Deliverable: Top three recommendations based on analysis

Purpose of advertising

  • Position membership as an enhancement to the existing leisure experience.

  • Showcase testimonials from members who use Cyclistic for recreational purposes.

  • Highlight the convenience of unlimited rides, extended trip durations, and cost savings.

Advertising:

  • Place ads on relevant online platforms (e.g., travel blogs, event pages, dining and entertainment guides).

  • Promote Cyclistic at major Chicago events such as St. Patrick’s Day, the Chicago Blues Festival, the Air & Water Show, and Pride, offering limited-time festival passes (“Skip the traffic with a special Lollapalooza festival pass!”).

  • Utilize geo-targeted mobile ads near high-traffic casual rider stations with messages like “Ride unlimited this weekend – try a membership today!”

  • Follow-up email marketing (“You rode three times this month. A membership would’ve saved you $X!”).

  • Engage influencers and travel vloggers to create content showcasing scenic rides and group bike tours (e.g., “Museum Ride” or “Scenic Chicago Ride”).

Promotions:

  • Weekend Membership Plans: Introduce a “Leisure Package” offering extended weekend ride times.

  • Seasonal Discounts: Launch summer promotions when ridership peaks.

  • Referral Bonuses: Encourage existing members to invite friends by offering incentives. Casual riders frequently bike in groups.

  • Trial Memberships: Offer discounted first-month trials to entice casual users (“First month free!”).

  • Partnership Discounts: Collaborate with local attractions (e.g., discounted admission to museums or restaurants for members).

  • Gamification & Rewards: Implement a leaderboard tracking miles ridden among friends (similar to Strava) and reward milestones with merchandise or free guest passes.

Commuter crossover marketing.

  • Highlight the convenience and cost-effectiveness of commuting by bike (“Beat rush hour traffic!”).

  • Offer free trial passes to potential commuters during peak weekday hours, 8am and 5pm.

  • Establish corporate and student membership partnerships with discounts for employees and young professionals.

  • Develop a transit partnership with CTA (Chicago Transit Authority) for bundled bike-and-train/bus memberships.

Conclusion

The analysis reveals clear distinctions between casual riders and annual members, providing a strategic opportunity for conversion. By emphasizing leisure benefits, implementing targeted digital marketing, and introducing promotional incentives, Cyclistic can effectively increase membership conversion rates while enhancing the overall rider experience.